Machine Learning Algorithms for Slow Fashion Consumer Prediction: Theoretical and Managerial Implications

Abstract

Purpose: To compare, propose, and discuss the implications of five machine learning algorithms for predicting Slow fashion consumer profiles. Methodology/approach: We use the Python programming language to build the models with scikit-learn libraries. We tested the potential of five algorithms to correct classifier Slow fashion consumers: I) extremely randomized trees, II) random forest, III) support vector machine, IV) gradient boosting Tree, and V) naïve bayes. Originality/Relevance: This paper's originality lies in its combination of sustainability concerns, consumer behavior analysis, and machine learning techniques. It addresses a critical issue in the fashion industry and offers practical implications that can be beneficial for companies seeking to align their practices with Slow fashion principles. This interdisciplinary approach makes it a relevant contribution to both academia and industry.  Key findings: The performance metrics revealed satisfactory values for all algorithms. Nevertheless, the support vector machine presents a better precision (96%) on the dataset for Slow fashion consumer profiling, while random forest performs the worst (87%). Theoretical/methodological contributions: We understood that the model can be helpful for companies that wish to adopt more targeted and practical approaches in the context of Slow fashion, allowing them to make more informed and strategic decisions. Therefore, these insights can guide future research in optimizing machine learning applications for consumer behavior analysis and provide valuable guidance for fashion marketers seeking to enhance their targeting and engagement strategies.